Objective: To analyse the relationship between the index from EEG signals wavelet transformation and the serious degree of OSAHS and explore the value of wavelet transform processing on EEG signals of OSAHS patients. Methods: All subjects were monitored by polysomnograpgy(PSG), and they were divided subsequently into the control (non-OSAHS), mild, moderate and severe groups according to their AHI. Firstly EEG data was transformed to EDP form, and then Morlet wavelet function was used in the wavelet transformalion to process the EEG signals associated with sleep breathing. Based on the wavelet coefficient diagram in the frequency- time domain, some indexes related to the average energy were proposed to describe the serious degree of OSAHS in the patients. Results: Compared with controls,among the mild and moderate and severe groups OSAHS had significant defference in indexes P1 ,P50,P10(Pdiv2≤1.0) and P20 (Pdiv2≥1.0) that derived from divl and div2 (P<0. 05) . In the multiple regression analysis between EEG indexes and clinical parameters,the most related element about P1 , P50 ,P10(P,div2≥1.0)and P20(Pdiv2≥1.0) was TAT/TST, apnea index, SpO2(90%T/TST)and apnea index, - 0. 602 respectively(regression coefficients were -0. 369, -0. 720, 0. 317, -0. 602 respectively, all (P <0. 05). Conclusion: OSAHS EEG average energy indexes div 1 and div 2 changed with serious degree of OSAHS and they may contribute to the identification of different degrees in patients with OSAHS especially in the severe group. All parameters( except for P10 ) are highly related to apnea indexes. Statis-tical analysis showed that wavelet transform is a good tool for the research of OSAHS EEG signals.%目的:利用小波转换方法对阻塞性睡眠呼吸暂停低通气综合征(OSAHS)患者脑电信号进行处理分析.方法:对人选142例鼾症患者分为对照组、轻度组、中度组和重度组.利用Morlet小波函数对脑电信号进行变换处理得到平均能量尺度图,通过对单位时间能量的计算得到脑电能量变异的两个指标div1和div2,并分别提取两个特征参数P1、P50和P10( P(div2)≥1.0)、P20( Pdiv2≥1.0).检验各个指标的正态分布性和各指标在各样本组间的差异性,以及各指标与临床指标的相关性.结果:发现变异指标的四个参数各组之间的检验结果均表现出相似的规律:重度组与对照、轻、中度组之间的差异均有显著意义(P<0.05);其它各组之间的差异无显著意义,但有显著性趋势(0.05展开▼
